Supervised classification aims at finding hidden patterns from a training sample of labeled data in order to predict the labels of out-of-sample data. Several methods have been proposed in order to construct highly predictive classification tools. Some of the most widely used methodologies are based on Deep Learning mechanisms [1]}, \(k\) -Nearest Neighborhoods [2]}, [3]}, Naïve Bayes [4]}, Classification Trees [5]}, [6]} and Support Vector Machines [7]}. The massive use of these tools has induced, in many situations, that malicious adversaries adaptively manipulate their data to mislead the outcome of an automatic analysis, and new classification rules must be designed to handle the possibility of this noise in the training labels. A natural example are the spam filters for emails, where malicious emails are becoming more difficult to automatically be detected since they have started to incorporate patterns that typically appear in legitimate emails. As a consequence, the development of robust methods against these kind of problems has attracted the attention of researchers (see e.g., [8]}, [9]}).
